Read-source requests to support bundled writes in a distributed storage system

ABSTRACT

A method for execution by processing modules of one or more computing devices of a dispersed storage network (DSN), the method begins by identifying a stored data object (using bundled writes) for retrieval from a dispersed storage network (DSN), determining a DSN address that corresponds to the store data object, generating a read source request based on the DSN address, identifying a set of storage units of the DSN, where one or more of the storage units of the set of storage units are associated with storage of the stored data object, sending the read source request to the identified set of storage units, receiving retrieved encoded data slices from at least some of the storage units of the identified set of storage units and dispersed storage error decoding, for each set of encoded data slices, a decode threshold of received encoded data slices to produce a recovered data object.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U. S.C. § 120, as a continuation-in-part of U.S. Utility patentapplication Ser. No. 15/671,746, entitled “STORING AND RETRIEVING DATAUSING PROXIES,” filed Aug. 8, 2017, which claims priority pursuant to 35U.S.C. § 120 as a continuation-in-part of U.S. Utility application Ser.No. 14/955,200, entitled “STORING DATA USING A DUAL PATH STORAGEAPPROACH” filed Dec. 1, 2015, now issued as U.S. Pat. No. 9,740,547 onAug. 22, 2017, which claims priority pursuant to 35 U.S.C. § 119(e) toU.S. Provisional Application No. 62/109,700, entitled “REDUNDANTLYSTORING DATA IN A DISPERSED STORAGE NETWORK,” filed Jan. 30, 2015, allof which are hereby incorporated herein by reference in their entiretyand made part of the present U.S. Utility Patent Application for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIGS. 9-9A are schematic block diagrams of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 9B is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention; and

FIG. 9C is a flowchart illustrating an example of recovering data inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30,32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-9C. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generateper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

In one embodiment, “bundled writes” is a technique to achieve improvedreliability and at the same time decrease required rebuilding. Bundledwrites is an approach that enables more slices to be written then thereare available (or performant) dispersed or distributed storage (DS)units. When a DS processing unit determines that some DS units areunavailable, have not confirmed a write, are not keeping up, orotherwise unable to receive slices or receive them in a timely manner,then the DS processing unit can make a determination to apply bundledwrites.

When performing a read operation against a system supporting bundledwrites, one change is that the read request that indicates a particularslice name to read is no longer adequate in a system supporting bundledwrites, as the names for slices which may be present on a particulardistributed or dispersed storage (DS) unit can no longer be predicted.While one approach would be to send a width-number of Read-Slicerequests to every DS unit, (one request for each possible slice name),this is wasteful in terms of network bandwidth, request serializationand deserialization, process, and IO/seek operations. In one embodiment,to overcome the need to send a width number of read-slice requests toeach DS unit, a “Read-Source” request is included, which has a semanticthat a DS unit which receives a Read-Source request must return allslices it has whose source name matches the source name provided in therequest.

FIGS. 9-9A are schematic block diagrams of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 (computing device) of FIG. 1, thenetwork 24 of FIG. 1, and a DST execution (EX) unit set 440. The DSTexecution unit set 440 includes a set of DST execution units, where eachDST execution unit is affiliated with a unique encoded data slice of aset of encoded data slices for storage where data is dispersed storageerror encoded in accordance with dispersal parameters to produce aplurality of sets of encoded data slices. For example, the DST executionunit set includes DST execution units 1-5 when the dispersal parametersinclude an information dispersal algorithm (IDA) width of n=5. Each DSTexecution unit may be implemented utilizing the storage unit 36 of FIG.1.

FIG. 9 illustrates an example operation of storing data using bundledwrites, where the DST processing unit 16 dispersed storage error encodesa data object for storage in the DST execution unit set to produce aplurality of sets of encoded data slices, where each encoded data sliceof each set of encoded data slices is mapped to a unique DST executionunit of the DST execution unit set in accordance with a slice mapping.For each set of encoded data slices, the DST processing unit 16identifies an encoded data slice associated with the selected DSTexecution unit to produce a bundled encoded data slice of a plurality ofbundled encoded data slices. For example, the DST processing unit 16identifies the encoded data slice based on the slice mapping. Forinstance, the DST processing unit 16 identifies encoded data slices 3-1through 3-4 corresponding to a third pillar encoded data sliceassociated with four sets of encoded data slices as the plurality ofbundled encoded data slices.

Having produced the bundled encoded data slices, the DST processing unit16 updates the slice mapping based on the plurality of bundled encodeddata slices to produce an updated slice mapping. As a specific example,the DST processing unit 16 selects a distribution approach. Thedistribution approach maps each bundled encoded data slice of theplurality of bundled encoded data slices to at least one other DSTexecution unit of the DST execution unit set. The distribution approachincludes one or more of even distribution amongst available DSTexecution units, distribution of more bundled encoded data slices to DSTexecution units associated with a highest level of performance, anddistribution of bundled encoded data slices amongst DST execution unitsimplemented at different sites.

Having updated the slice mapping, the DST processing unit 16 selects asubset of DST execution units of the set of DST execution units forstorage of the plurality of bundled encoded data slices in accordancewith the updated slice mapping. The selecting may be based on one ormore of DST execution unit performance levels, a predetermination, orinterpreting system registry information. As a specific example, the DSTprocessing unit 16 determines the updated slice mapping based on thedistribution approach. For instance, the DST processing unit 16 mapsbundled encoded data slice 3-1 to DST execution unit 1, maps bundledencoded data slice 3-2 to DST execution unit 2, maps bundled encodeddata slice 3-3 to DST execution unit 4, and maps bundled encoded dataslice 3-4 to DST execution unit 5 when the distribution approachincludes the even distribution of the bundled encoded data slices.

Having selected the subset of DST execution units (e.g., DST executionunits 1-2, 4-5), the DST processing unit 16, for each DST execution unitof the subset of DST execution units, issues, via the network 24, awrite slice request that includes a group of encoded data slices inaccordance with the updated slice mapping. For example, the DSTprocessing unit 16 issues, via the network 24, a write slice request 1that includes encoded data slices 1-1 through 1-4 and bundled encodeddata slice 3-1.

FIG. 9A illustrates further steps of the example of operation of thestoring of the data where the DST processing unit 16. The DST processingunit 16 facilitates migration of the plurality of bundled encoded dataslices from the subset of DST execution units to the selected DSTexecution unit. As a specific example, each DST execution unit of thesubset of DST execution units issues a write slice request to theselected DST execution unit, where the write slice request includes acorresponding bundled encoded data slice. As another specific example,the identified DST execution unit issues read slice requests to thesubset of DST execution units and receives read slice responses thatincludes the plurality of bundled encoded data slices. As anotherspecific example, the DST processing unit 16 issues delete slicerequests to the subset of DST execution units to delete the plurality ofbundled encoded data slices when confirming that the selected DSTexecution unit has successfully non-temporarily stored the plurality ofbundled encoded data slices.

FIG. 9B is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 (computing device) of FIG. 1, thenetwork 24 of FIG. 1, and a DST execution (EX) unit set 464. The DSTexecution unit set 464 may be implemented utilizing the DST executionunit set 440 of FIG. 9. The DST execution unit set includes a set of DSTexecution units, where each DST execution unit is affiliated with aunique encoded data slice of a set of encoded data slices for storagewhere data is dispersed storage error encoded in accordance withdispersal parameters to produce a plurality of sets of encoded dataslices. The stored data is stored at least partially using one or morebundled writes techniques as discussed in FIGS. 9 and 9A. For example,the DST execution unit set 464 includes DST execution units 1-5 when thedispersal parameters include an information dispersal algorithm (IDA)width of n=5. The DSN functions to recover data that has been stored inthe DST execution unit set. Each DST execution unit may be implementedutilizing the storage unit(s) 36 of FIG. 1.

In an example of operation of the recovering of the data, the DSTprocessing unit 16 identifies a stored data object for retrieval fromthe DST execution unit set 464 to produce a data identifier (ID), wherethe data object is dispersed storage error encoded to produce aplurality of sets of encoded data slices and where the plurality of setsof encoded data slices are stored in the DST execution unit set 464.Each encoded data slice is associated with a unique slice name and eachslice name includes a common source name. The identifying includes atleast one of interpreting a request and performing a lookup.

Having identified the stored data object for retrieval, the DSTprocessing unit 16 determines a DSN address that corresponds to thestored data object. The DSN address includes a virtual addressassociated with the storage of the stored data object. The virtualaddress includes a common source name. As an example of the determiningof the DSN address, the DST processing unit 16 interprets an entry of adispersed hierarchical index based on the data ID to identify the commonsource name. As another example of the determining of the DSN address,the DST processing unit 16 interprets a DSN directory based on the dataID to identify the common source name.

Having determined the DSN address, the DST processing unit 16 generatesa read source request 466 based on the DSN address. For example, the DSTprocessing unit 16 populates a source name field of the read sourcerequest 466 with the identified common source name. Having generated theread source request 466, the DST processing unit 16 identifies a set ofDST execution units (e.g., of the DST execution unit set). Theidentifying includes at least one of interpreting a DSN address tophysical location table and interpreting DST execution unit status. Forinstance, the DST processing unit 16 identifies DST execution units 1-5as the set of DST execution units based on the DSN address to physicallocation table.

Having identified the set of DST execution units, the DST processingunit 16 sends, via the network 24, the read source request 466 to theidentified set of DST execution units. Having sent the read sourcerequest 466, the DST processing unit 16 receives, via the network 24,retrieved encoded data slices 468 from at least some of the DSTexecution units of the identified set of DST execution units.

For each set of encoded data slices, the DST processing unit 16dispersed storage error decodes a decode threshold number of receivedencoded data slices to reproduce a data segment corresponding to the setof encoded data slices. Having reproduced the data segment, the DSTprocessing unit 16 aggregates a plurality of reproduced data segments toproduce a recovered data object 470.

FIG. 9C is a flowchart illustrating an example of recovering data. Themethod includes step 476 where a processing module (e.g., of adistributed storage and task (DST) processing unit) identifies a storeddata object for retrieval from a dispersed storage network (DSN). Theidentifying includes at least one of interpreting a request orperforming a lookup. The stored data object is stored at least partiallyusing one or more bundled writes techniques as discussed in FIGS. 9 and9A.

The method continues at step 478 where the processing module determinesa DSN address that corresponds to the store data object. The determiningincludes one or more of interpreting a dispersed hierarchical index,interpreting a DSN directory, or identifying a source name common to allslice names of encoded data slices of the store data object.

The method continues at step 480 where the processing module generates aread source request based on the DSN address. For example, theprocessing module generates the read source request to include thesource name that is common to all slice names of encoded data slices ofthe store data object.

The method continues at step 482 where the processing module identifiesa set of storage units of the DSN, where one or more of the storageunits of the set of storage units are associated with storage of thestored data object. The identifying includes one or more of issuing aquery, interpreting a query response, interpreting an error message,interpreting a storage unit status, or performing a lookup.

The method continues at step 484 where the processing module sends theread source request to the identified set of storage units. For example,the processing module replicates the read source request and transmits areplicated resource request to each storage unit of the identified setof storage units.

The method continues at step 486 where the processing module receivesretrieved encoded data slices from at least some of the storage units ofthe identified set of storage units. For example, the processing modulereceives read slice responses and extracts one or more retrieved encodeddata slices from the received read slice responses.

For each set of encoded data slices, the method continues at step 488where the processing module dispersed storage error decodes a decodethreshold number of received encoded data slices to produce a recovereddata object. For example, for each set of encoded data slices, theprocessing module identifies a decode threshold number of receivedencoded data slices dispersed storage error decodes the decode thresholdnumber of encoded data slices to reproduce a data segment of a pluralityof data segments of the data object, and aggregates the reproducedplurality of data segments to produce the recovered data object.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other computing devices. In addition, at least one memorysection (e.g., a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices of the dispersedstorage network (DSN), cause the one or more computing devices toperform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: identifying a stored data object forretrieval from a dispersed storage network (DSN), wherein the storeddata object is stored at least partially as a bundled write; determininga DSN address that corresponds to the stored data object; generating aread source request based on the DSN address, wherein the read sourcerequest includes a common source name; identifying a set of storageunits of the DSN, wherein one or more storage units of the set ofstorage units are associated with the common source name; sending theread source request to the identified set of storage units to retrieveencoded data slices associated with the common source name; receivingthe retrieved encoded data slices from at least some of the storageunits of the identified set of storage units, wherein each storage unitthat receives the read source request returns all stored encoded dataslices with a source name that matches the common source name providedin the read source request; and disperse storage error decoding, foreach set of the encoded data slices, a decode threshold number ofreceived encoded data slices to produce a recovered data object.
 2. Themethod of claim 1, wherein the identifying a stored data object forretrieval includes at least one of interpreting a request or performinga lookup.
 3. The method of claim 1, wherein the determining a DSNaddress includes one or more of interpreting a dispersed hierarchicalindex, interpreting a DSN directory, or identifying a source name commonto all slice names of encoded data slices of the store data object. 4.The method of claim 1, wherein the generating the read source requestincludes a source name that is common to all slice names of encoded dataslices of the store data object.
 5. The method of claim 1, wherein theidentifying a set of storage units of the DSN includes one or more ofissuing a query, interpreting a query response, interpreting an errormessage, interpreting a storage unit status, or performing a lookup. 6.The method of claim 1, wherein sending the read source request to theidentified set of storage units includes replicating the read sourcerequest and transmitting a replicated resource request to each storageunit of the identified set of storage units.
 7. The method of claim 1,wherein the receiving the retrieved encoded data slices includesreceiving read slice responses and extracting one or more retrievedencoded data slices from the received read slice responses.
 8. Themethod of claim 1, wherein the dispersed storage error decoding, foreach set of encoded data slices, includes identifying a decode thresholdnumber of received encoded data slices, dispersed storage error decodingthe decode threshold number of received encoded data slices to reproducea data segment of a plurality of data segments of the stored dataobject, and aggregating the received encoded data slices to produce therecovered data object.
 9. A computing device of a group of computingdevices of a dispersed storage network (DSN), the computing devicecomprises: an interface; a local memory; and a processing moduleoperably coupled to the interface and the local memory, wherein theprocessing module is configured to: identify a stored data object,stored as encoded data slices, for retrieval from a dispersed storagenetwork (DSN), wherein the stored data object is stored at leastpartially as a bundled write; determine a DSN address that correspondsto the stored data object; generate a read source request based on theDSN address, wherein the read source request includes a common sourcename, wherein the generate a read source request includes a source namethat is common to all slice names of the encoded data slices of thestored data object; identify a set of storage units of the DSN, whereinone or more storage units of the set of storage units are associatedwith the common source name; send the read source request to theidentified set of storage units to retrieve encoded data slicesassociated with the common source name; receive the retrieved encodeddata slices from at least some of the storage units of the identifiedset of storage units, wherein each storage unit that receives the readsource request returns all stored encoded data slices with a source namethat matches the common source name provided in the read source request;and disperse storage error decode, for each set of the encoded dataslices, a decode threshold number of received encoded data slices toproduce a recovered data object.
 10. The computing device of claim 9,wherein the identify a stored data object for retrieval includes atleast one of interpreting a request or performing a lookup.
 11. Thecomputing device of claim 9, wherein the determine a DSN addressincludes one or more of interpreting a dispersed hierarchical index,interpreting a DSN directory, or identifying a source name common to allslice names of the encoded data slices of the stored data object. 12.The computing device of claim 9, wherein the identify a set of storageunits of the DSN includes one or more of issuing a query, interpreting aquery response, interpreting an error message, interpreting a storageunit status, or performing a lookup.
 13. The computing device of claim9, wherein the send the read source request to the identified set ofstorage units includes replicating the read source request andtransmitting a replicated resource request to each storage unit of theidentified set of storage units.
 14. The computing device of claim 9,wherein the receive the retrieved encoded data slices includes receivingread slice responses and extracting one or more retrieved encoded dataslices from the received read slice responses.
 15. The computing deviceof claim 9, wherein the disperse storage error decode, for each set ofthe encoded data slices, includes identifying a decode threshold numberof received encoded data slices, dispersed storage error decoding thedecode threshold number of received encoded data slices to reproduce adata segment of a plurality of data segments of the stored data object,and aggregating the received encoded data slices to produce therecovered data object.
 16. A system, comprises: an interface; a localmemory; and a processing module operably coupled to the interface andthe local memory, wherein the processing module is configured to:identify a stored data object for retrieval from a dispersed storagenetwork (DSN), wherein the stored data object is stored at leastpartially as a bundled write; determine a DSN address that correspondsto the stored data object; generate a read source request based on theDSN address, wherein the read source request includes a common sourcename; identify a set of storage units of the DSN, wherein one or morestorage units of the set of storage units are associated with the commonsource name; send the read source request to the identified set ofstorage units to retrieve encoded data slices associated with the commonsource name, wherein the send the read source request to the identifiedset of storage units includes replicating the read source request andtransmitting a replicated resource request to each storage unit of theidentified set of storage units; receive the retrieved encoded dataslices from at least some of the storage units of the identified set ofstorage units, wherein each storage unit that receives the read sourcerequest returns all stored encoded data slices with a source name thatmatches the common source name provided in the read source request; anddisperse storage error decode, for each set of the encoded data slices,a decode threshold number of received encoded data slices to produce arecovered data object.
 17. The system of claim 16, wherein the determinea DSN address includes one or more of interpreting a dispersedhierarchical index, interpreting a DSN directory, or identifying asource name common to all slice names of encoded data slices of thestored data object.
 18. The system of claim 16, wherein the generate aread source request includes a source name that is common to all slicenames of encoded data slices of the stored data object.